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PUBLICATIONS

Peer-reviewed Articles

  1. Taggart, T., Ransome, Y., Andreou, A., Song, I., Kershaw, T., & Milburn, N. Accepted. Utilizing activity space assessments to investigate neighborhood exposure to racism-related stress and related substance use risk among young Black men. American Journal of Public Health.

  2. Song, I., & Kim, D. In press. Three common machine learning algorithms neither enhance higher prediction accuracy nor reduce spatial autocorrelation in residuals: An analysis of twenty-five socio-economic data sets. Geographical Analysishttps://doi.org/10.1111/gean.12351

  3. Jun, Y.-B., Song, I., Kim, O.-J. and S.-Y. Kim. (2022). Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study. Journal of Exposure Science and Environmental Epidemiology 32, 637-643. https://doi.org/10.1038/s41370-022-00412-1

  4. Song, I. and H. Luan. (2022). The spatially and temporally varying association between mental illness and substance use mortality and unemployment: a Bayesian analysis in the contiguous United States, 2001-2014. Applied Geography 140, 102664. https://doi.org/10.1016/j.apgeog.2022.102664

  5. Ransome, Y., Luan, H., Song, I., Fiellin, D. A. and S. Galea. (2022). Poor mental health days are associated with COVID-19 infection rates in the USA. American Journal of Preventive Medicine 62(3), 326-332. https://doi.org/10.1016/j.amepre.2021.08.032

  6. Luan, H., Song, I., Fiellin, D. and Y. Ransome. (2021). HIV infection prevalence significantly intersects with COVID-19 infection at the area-level: a USA county-level analysis. Journal of Acquired Immune Deficiency Syndromes 88(2), 125-131. https://doi.org/10.1097/QAI.0000000000002758

  7. Kim, D. and I. Song. (2021). Predicting model improvement by accounting for spatial autocorrelation: A socio-economic perspective. The Professional Geographer 73(1), 131-149. https://doi.org/10.1080/00330124.2020.1812408

  8. Song, I., Kim, O.-J., Choe, S.-A. and S.-Y. Kim. (2020). Spatial heterogeneity in the association between particulate matter air pollution and low birth weight in South Korea. Environmental Research 191, 110096. https://doi.org/10.1016/j.envres.2020.110096

  9. Park, Y., Song, I., Yi, J., Yi, S.-J. and S.-Y. Kim. (2020). Web-based visualization of scientific research findings: national-scale distribution of air pollution in South Korea. International Journal of Environmental Research and Public Health 17(7): 2230. https://doi.org/10.3390/ijerph17072230

  10. Kim, D., Lee, J.-Y., Seo, J. and I. Song. (2019). Recolonization of native and invasive plants after large-scale clearance of a temperate coastal dunefield. Applied Geography 109, 102030. https://doi.org/10.1016/j.apgeog.2019.05.007

  11. Song, I., Lee, C. and K.-H. Park. (2018). An ensemble machine learning from spatio-temporal Kriging for imputation of PM10 in Seoul, Korea. Journal of the Korean Geographical Society 53(3): 427-444. https://journal.kgeography.or.kr/articles/pdf/vMLR/geo-2018-053-03-9.pdf

  12. Kim, S.-Y. and I. Song. (2017). National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea. Environmental Pollution 226(2017): 21-29. https://doi.org/10.1016/j.envpol.2017.03.056

  13. Song, I. and S.-Y. Kim. (2016). Estimation of representative area-level concentrations of particulate matter (PM10) in Seoul, Korea. Journal of the Korean Association of Geographic Information Studies 19(4): 118-129. (in Korean, English abstract available) https://doi.org/10.11108/kagis.2016.19.4.118

  14. Eum, Y., Song, I., Kim, H.-C., Leem, J.-H. and S.-Y. Kim. (2015). Computation of geographic variables for air pollution prediction models in South Korea. Environmental Health and Toxicology 30: 70-83. https://doi.org/10.5620/eht.e2015010

Other publications

  1. Ransome, Y., Song, I., Pham, L., & Busette, C. (2022). Churches are closing in predominantly Black communities – why public health officials should be concerned. The Brookings Institution. Published May 3, 2022.

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